神经信息检索 (IR) 模型的最新进展极大地提高了它们在各种 IR 任务中的有效性。这些进步使神经 IR 模型能够更好地理解和检索响应用户查询的相关信息。然而，为了确保这些模型在实际应用中的可靠性，需要关注它们的鲁棒性，这已成为一个越来越重要的研究领域。

🤖 **神经信息检索模型的鲁棒性**：神经推理模型的弹性对其在现实世界情况下的可靠性能至关重要。鲁棒性指的是模型在各种意外情况下保持一致和弹性运行的能力。这包括处理超出分布 (OOD) 的情况、防范对抗性攻击以及减少跨请求的性能差异。考虑到这些模型面临的各种挑战，合成最新发现并从公认的做法中得出结论至关重要。

💪 **信息检索中的鲁棒性**：在信息检索中，鲁棒性是一个复杂的概念，包含各种重要的因素，具体如下： * **对抗性攻击**：这些是故意试图向 IR 系统提供虚假信息或请求以操纵它的行为。为了保护搜索结果的完整性，鲁棒模型需要能够识别和抵消此类攻击。 * **OOD 场景**：IR 模型经常遇到现实世界应用训练数据集不存在的数据。为了获得可靠的结果，鲁棒模型需要能够成功地泛化到这些未知的查询和文档。 * **性能差异**：这描述了模型在各种查询中的一致性。对于可行的 IR 模型，即使在不理想的情况下也应该看到最小的性能下降。

🚀 **提高密集检索模型和神经排序模型的鲁棒性**：在密集检索模型 (DRM) 和神经排序模型 (NRM) 的背景下，它们是神经 IR 管道的重要组成部分，最近的一项研究强调了对抗性和 OOD 鲁棒性。DRM 首先检索相关文档，然后 NRM 根据它们与查询的相关性对这些文档进行排序。提高这些模型的弹性对于确保 IR 系统的整体可靠性至关重要。

📊 **全面分析和基准测试**：该研究对应用于稳健的神经信息检索模型研究的现有方法、数据库和评估标准进行了全面分析。通过对这些元素的分析，该研究提到了该领域面临的挑战和未来的潜在途径，尤其是在大型语言模型时代。该分析旨在为从事 IR 系统稳健性研究的学者和从业人员提供有用的见解。

🧪 **BestIR 基准**：该团队提供了名为 BestIR 的鲁棒 IR 基准，这是一个异构评估基准，旨在评估神经信息检索模型的弹性。可以在 https://github.com/Davion-Liu/BestIR 访问该基准。

🌟 **主要贡献**：该研究极大地推进了稳健的神经信息检索 (IR) 的研究。该综述提供了对 IR 中鲁棒性现有研究的广泛概述和分类。本文通过在该背景下定义鲁棒性并将其特征化为不同的类别，有助于更深入地了解该领域。这种系统的方法支持稳健的脑 IR 系统的长期发展。

📈 **评估指标、数据集和程序**：该研究探索了与 IR 中鲁棒性的不同方面相关的评估指标、数据集和程序。该研究整合了调查中描述的当前数据集，并通过对这些组件的全面描述提供了 BestIR 基准。这种新的评估工具提供了一个标准化的框架，用于评估和对比各种 IR 模型的鲁棒性。

Recent developments in neural information retrieval (IR) models have greatly improved their effectiveness across various IR tasks. These advancements have made neural IR models more capable of understanding and retrieving relevant information in response to user queries. However, ensuring the reliability of these models in practical applications requires a focus on their robustness, which has become an increasingly significant area of research.

Neural inference models’ resilience is essential to their dependable performance in real-world situations. Robustness refers to the model’s capacity to continue operating consistently and resiliently in a variety of unexpected situations. This includes managing out-of-distribution (OOD) situations, guarding against adversarial attacks, and reducing performance variance across requests. Considering the range of difficulties these models encounter, it is critical to synthesize recent findings and draw conclusions from accepted practices.

In information retrieval, robustness is a complex notion that includes various important elements, which are as follows.

Adversarial Attacks: These are intentional attempts to provide false information or requests into the IR system in order to manipulate it. In order to preserve the integrity of the search results, robust models need to be able to recognize and counteract these kinds of attacks.

OOD Scenarios: IR models often face data that is not present in real-world application training datasets. For reliable outcomes, robust models need to be able to generalize successfully to these unknown questions and documents.

Performance Variance: This describes how well the model performs consistently across various queries. Minimal performance degradation should be seen even under less-than-ideal situations for a viable IR model.

In the context of dense retrieval models (DRMs) and neural ranking models (NRMs), which are essential parts of the neural IR pipeline, a recent study has highlighted adversarial and OOD robustness. Relevant documents are first retrieved by DRMs and then ranked by NRMs according to how relevant they are to the query. Improving the resilience of these models is essential to guaranteeing the IR system’s general dependability.

The study offered a thorough analysis of the current approaches, databases, and assessment criteria applied to the research of resilient neural information retrieval models. Through an analysis of these elements, the study has mentioned the difficulties and potential paths ahead in this domain, especially in the age of massive language models. The purpose of this analysis is to provide scholars and practitioners who are working on the resilience of IR systems with useful insights.

The team has provided the Benchmark for robust IR called BestIR, which is a heterogeneous evaluation benchmark intended to evaluate the resilience of neural information retrieval models. The benchmark can be accessed at https://github.com/Davion-Liu/BestIR. 

The team has summarized their primary contribution as follows.

The study has significantly advanced the subject of robust neural information retrieval (IR). The review provides an extensive overview and classification of the existing research on robustness in IR. The paper contributes to a greater understanding of the area by providing a definition of robustness in this context and characterizing it into different categories. This methodical approach supports the long-term evolution of robust brain IR systems.

The study explores the evaluation metrics, datasets, and procedures related to different facets of robustness in IR. The research integrates current datasets described in the survey and offers the BestIR benchmark by providing a thorough description of these components. This new assessment tool offers a standardized framework for evaluating and contrasting the robustness of various IR models.

---

Check out the Paper and GitHub. All credit for this research goes to the researchers of this project. Also, don’t forget to follow us on Twitter. 

Join our Telegram Channel and LinkedIn Group.

If you like our work, you will love our newsletter..

Don’t Forget to join our 46k+ ML SubReddit

The post Advancing Robustness in Neural Information Retrieval: A Comprehensive Survey and Benchmarking Framework appeared first on MarkTechPost.